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Impact of Grid Computing on 
Network Operators and HW Vendors 
Hot Interconnect @ Stanford 
Tal Lavian 
tlavian@ieee.org 
Advanced Technology Research , Nortel Networks 
August 18th, 2005
2 
Optical Networks Change the Current Pyramid 
George Stix, 
Scientific American, 
January 2001 
x10 
DWDM- fundamental miss-balance between computation and communication 
5 Years – x10 gap, 10 years- x100 gap
Waste Bandwidth 
“A global economy designed to waste 
transistors, power, and silicon area -and 
conserve > 
bandwidth above all- is breaking apart 
and reorganizing itself to waste bandwidth and 
conserve power, silicon area, and transistors.“ 
George Gilder Telecosm 
3
The “Network” is a Prime Resource for Large- Scale 
Distributed System 
4 
Network 
Computation 
Instrumentation 
Person 
Visualization 
Storage 
Integrated SW System Provide the “Glue” 
Dynamic optical network as a fundamental Grid service in 
data-intensive Grid application, to be scheduled, to be 
managed and coordinated to support collaborative 
operations
From Super-computer to Super-network 
>In the past, computer processors were the fastest part 
• peripheral bottlenecks 
>In the future optical networks will be the fastest part 
• Computer, processor, storage, visualization, and 
5 
instrumentation - slower "peripherals” 
> eScience Cyber-infrastructure focuses on computation, storage, 
data, analysis, Work Flow. 
• The network is vital for better eScience
6 
Grid Network Limitations in L3 
> Radical mismatch between the optical transmission world and the 
electrical forwarding/routing world. 
• Currently, a single strand of optical fiber can transmit more bandwidth 
than the entire Internet core. 
> Transmit 1.5TB over 1.5KB packet size 
1 Billion identical lookups 
> Mismatch between L3 core capabilities and disk cost 
• With $2M disks (6PB) can fill the entire core internet for a year 
> L3 networks can’t handle these amounts effectively, predictably, in a 
short time window 
• L3 network provides full connectivity -- major bottleneck 
• Apps optimized to conserve bandwidth and waste storage 
• Network does not fit the “e-Science Workflow” architecture 
Prevents true Grid Virtual Organization (VO) research collaborations
Lambda Grid Service 
Need for Lambda Grid Service architecture that 
interacts with Cyber-infrastructure, and overcome 
data limitations efficiently & effectively by: 
• treating the “network” as a primary resource just like 
7 
“storage” and “computation” 
• treat the “network” as a “scheduled resource” 
• rely upon a massive, dynamic transport infrastructure: 
Dynamic Optical Network
Generalization and Future Direction for 
Research 
> Need to develop and build services on top of the base encapsulation 
> Lambda Grid concept can be generalized to other eScience apps 
which will enable new way of doing scientific research where 
bandwidth is “infinite” 
> The new concept of network as a scheduled grid service presents new 
and exciting problems for investigation: 
8 
• New software systems that is optimized to waste bandwidth 
• Network, protocols, algorithms, software, architectures, systems 
• Lambda Distributed File System 
• The network as a Large Scale Distributed Computing 
• Resource co/allocation and optimization with storage and computation 
• Grid system architecture 
• enables new horizon for network optimization and lambda scheduling 
• The network as a white box, Optimal scheduling and algorithms
Enabling new degrees of App/Net coupling 
> Optical Packet Hybrid 
9 
• Steer the herd of elephants to ephemeral optical circuits (few to few) 
• Mice or individual elephants go through packet technologies (many to many) 
• Either application-driven or network-sensed; hands-free in either case 
• Other impedance mismatches being explored (e.g., wireless) 
> Application-engaged networks 
• The application makes itself known to the network 
• The network recognizes its footprints (via tokens, deep packet inspection) 
• E.g., storage management applications 
> Workflow-engaged networks 
• Through workflow languages, the network is privy to the overall “flight-plan” 
• Failure-handling is cognizant of the same 
• Network services can anticipate the next step, or what-if’s 
• E.g., healthcare workflows over a distributed hospital enterprise 
DRAC - Dynamic Resource Allocation Controller
10 
Teamwork 
from/to peering DRACs 
Application 
Admin. 
supply 
dynamic provisioning plane 
virtualization plane 
connectivity plane 
Alert, Adapt, 
Route, Accelerate 
Detect 
supply 
events 
events 
Agile 
Network(s) 
Application(s) 
AAA 
NE 
demand 
Negotiate 
DRAC, portable SW
SC2004 CONTROL CHALLENGE 
Application Application 
control 
* Dynamic Resource Allocation Controller 
11 
Services Services Services 
data 
control 
data 
Chicago Amsterdam 
AAA 
AAA AAA AAA 
DRAC DRAC DRAC 
DRAC 
* 
OOMMNNInIneett 
ODIN 
SSNNMMPP AASSTTNN 
Starligh 
Starligh 
t 
t 
Netherligh 
t UvA UvA 
Netherligh 
t 
• finesse the control of bandwidth across multiple domains 
• while exploiting scalability and intra- , inter-domain fault recovery 
• thru layering of a novel SOA upon legacy control planes and NEs
12 
Grid Network Services 
www.nortel.com/drac 
AA 
BB 
IInntteerrnneett ((SSllooww)) 
FFiibbeerr ((FFAA$$TT)) 
Grid Network Services 
www.nortel.com/drac 
GT4 GT4 
GT4 
GT4 
GT4 
GT4 
GW05 Floor 
AA AA 
OM3500 OM3500 
BB BB
Some key folks checking us out at our booth, 
GlobusWORLD ‘04, Jan ‘04 
13

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Impact of Grid Computing on Network Operators and HW Vendors

  • 1. Impact of Grid Computing on Network Operators and HW Vendors Hot Interconnect @ Stanford Tal Lavian tlavian@ieee.org Advanced Technology Research , Nortel Networks August 18th, 2005
  • 2. 2 Optical Networks Change the Current Pyramid George Stix, Scientific American, January 2001 x10 DWDM- fundamental miss-balance between computation and communication 5 Years – x10 gap, 10 years- x100 gap
  • 3. Waste Bandwidth “A global economy designed to waste transistors, power, and silicon area -and conserve > bandwidth above all- is breaking apart and reorganizing itself to waste bandwidth and conserve power, silicon area, and transistors.“ George Gilder Telecosm 3
  • 4. The “Network” is a Prime Resource for Large- Scale Distributed System 4 Network Computation Instrumentation Person Visualization Storage Integrated SW System Provide the “Glue” Dynamic optical network as a fundamental Grid service in data-intensive Grid application, to be scheduled, to be managed and coordinated to support collaborative operations
  • 5. From Super-computer to Super-network >In the past, computer processors were the fastest part • peripheral bottlenecks >In the future optical networks will be the fastest part • Computer, processor, storage, visualization, and 5 instrumentation - slower "peripherals” > eScience Cyber-infrastructure focuses on computation, storage, data, analysis, Work Flow. • The network is vital for better eScience
  • 6. 6 Grid Network Limitations in L3 > Radical mismatch between the optical transmission world and the electrical forwarding/routing world. • Currently, a single strand of optical fiber can transmit more bandwidth than the entire Internet core. > Transmit 1.5TB over 1.5KB packet size 1 Billion identical lookups > Mismatch between L3 core capabilities and disk cost • With $2M disks (6PB) can fill the entire core internet for a year > L3 networks can’t handle these amounts effectively, predictably, in a short time window • L3 network provides full connectivity -- major bottleneck • Apps optimized to conserve bandwidth and waste storage • Network does not fit the “e-Science Workflow” architecture Prevents true Grid Virtual Organization (VO) research collaborations
  • 7. Lambda Grid Service Need for Lambda Grid Service architecture that interacts with Cyber-infrastructure, and overcome data limitations efficiently & effectively by: • treating the “network” as a primary resource just like 7 “storage” and “computation” • treat the “network” as a “scheduled resource” • rely upon a massive, dynamic transport infrastructure: Dynamic Optical Network
  • 8. Generalization and Future Direction for Research > Need to develop and build services on top of the base encapsulation > Lambda Grid concept can be generalized to other eScience apps which will enable new way of doing scientific research where bandwidth is “infinite” > The new concept of network as a scheduled grid service presents new and exciting problems for investigation: 8 • New software systems that is optimized to waste bandwidth • Network, protocols, algorithms, software, architectures, systems • Lambda Distributed File System • The network as a Large Scale Distributed Computing • Resource co/allocation and optimization with storage and computation • Grid system architecture • enables new horizon for network optimization and lambda scheduling • The network as a white box, Optimal scheduling and algorithms
  • 9. Enabling new degrees of App/Net coupling > Optical Packet Hybrid 9 • Steer the herd of elephants to ephemeral optical circuits (few to few) • Mice or individual elephants go through packet technologies (many to many) • Either application-driven or network-sensed; hands-free in either case • Other impedance mismatches being explored (e.g., wireless) > Application-engaged networks • The application makes itself known to the network • The network recognizes its footprints (via tokens, deep packet inspection) • E.g., storage management applications > Workflow-engaged networks • Through workflow languages, the network is privy to the overall “flight-plan” • Failure-handling is cognizant of the same • Network services can anticipate the next step, or what-if’s • E.g., healthcare workflows over a distributed hospital enterprise DRAC - Dynamic Resource Allocation Controller
  • 10. 10 Teamwork from/to peering DRACs Application Admin. supply dynamic provisioning plane virtualization plane connectivity plane Alert, Adapt, Route, Accelerate Detect supply events events Agile Network(s) Application(s) AAA NE demand Negotiate DRAC, portable SW
  • 11. SC2004 CONTROL CHALLENGE Application Application control * Dynamic Resource Allocation Controller 11 Services Services Services data control data Chicago Amsterdam AAA AAA AAA AAA DRAC DRAC DRAC DRAC * OOMMNNInIneett ODIN SSNNMMPP AASSTTNN Starligh Starligh t t Netherligh t UvA UvA Netherligh t • finesse the control of bandwidth across multiple domains • while exploiting scalability and intra- , inter-domain fault recovery • thru layering of a novel SOA upon legacy control planes and NEs
  • 12. 12 Grid Network Services www.nortel.com/drac AA BB IInntteerrnneett ((SSllooww)) FFiibbeerr ((FFAA$$TT)) Grid Network Services www.nortel.com/drac GT4 GT4 GT4 GT4 GT4 GT4 GW05 Floor AA AA OM3500 OM3500 BB BB
  • 13. Some key folks checking us out at our booth, GlobusWORLD ‘04, Jan ‘04 13